A system analyzing device that executes process for analyzing a state of a system based on a sensor value acquired from a component of the system has been used.
For example, PTL 1 discloses a technique for generating a correlation model that is highly capable of detecting anomalies based on an actual measurement data of a plurality of types of performance values of a monitored device.
PTL 2 discloses a technique for extracting a correlation model for evaluating or predicting a prescribed event based on the data transmitted from a mobile machine.
PTL 3 discloses a technique for automatically visualizing a hierarchical relationship among items to be measured based on a measurement data of a plurality of characteristic values measured during a manufacturing process of a product.
Such analytical process is carried out for the purpose of safely and efficiently operating the system. One of the analytical process is a process for determining whether a state of the system is normal or anomalous through multivariable analysis of the sensor value. When the state of the system is determined to be anomalous, such analytical process notifies an operator or the system of information useful for identifying an anomaly factor. For example, only notifying of a sensor that shows an effect of the anomaly as the information useful for identifying the anomaly factor is effective for quickening an initial motion for identifying the anomaly factor.
Here, for example, the system is a unit or a mechanism composed of elements such as an ICT (Information and Communication Technology) system, a chemical plant, a power generation plant, and a power facility that affect one another.
Sensor values are various values acquired from components of the system. For example, the sensor value is a measured value acquired through a sensor provided in the components of the system. As such measured values, there are a valve opening, a liquid surface height, a temperature, a flowrate, pressure, a current, a voltage, etc. For example, the sensor value may be a predicted value calculated by using such a measured value. Further, for example, the sensor value may be a control signal generated from an information processing device so as to change the system to a desired operated state. Hereinafter, various values acquired from the components of the system will be simply referred to as sensor values without discriminating types.
In particular, in a system in which an effect of an occurrence of a failure on the economy, a human life, etc., is large, a function for notifying of information useful for identifying factors on anomalies not generated in the system in the past is important. The reason is that the larger an effect of a system failure is, the higher a possibility of a generated anomaly to become an unknown anomaly is for the system analysis device, because countermeasures are taken in advance to prevent the failure.
Examples of system analyzing techniques for notifying of information useful for identifying anomaly factors without being premised on the occurrence of similar anomalies in the past are described in PTLs 4-7.
The related technique disclosed in PTL 4 analyzes a system by using a plurality of regression equations. This related technique determines, for each regression equation, whether a prediction error has exceeded a threshold set for the regression equation. Then, the related technique outputs regression equations of which prediction errors have exceeded the threshold as candidates of an anomaly occurrence causes in descending order of the prediction errors.
The related technique disclosed in PTL 5 analyzes a system by using a Mahalanobis-Taguchi method. Then, this related technique outputs contribution of each data item to a Mahalanobis distance as an anomaly.
The related technique disclosed in PTL 6 analyzes a system by using principal component analysis. Then, this related technique outputs contribution of each data item to a Q statistical amount or Hoteling T2 dispersion as an anomaly.
The related technique disclosed in PTL 7 detects an anomaly of a process in a manufacturing system composed of a plurality of manufacturing devices. This related technique identifies an anomaly detection factor analysis rule applied to the process from a feature value of a process data acquired during process execution. Then, when a predicted value acquired by the anomaly detection factor analysis rule indicates an anomaly, this related technique notifies of the anomaly occurrence together with contribution of each data item to the anomaly.
Here, the “data item” is information relating to each of a plurality of types of sensor values acquired from the components of the system. For example, the “data item” may indicate a set of the sensor values relating to the type. Further, for example, the “data item” may indicate identification information for identifying a relating sensor value.